





import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
     from transformers.models.rag.configuration_rag import RagConfig
     from transformers.models.rag.tokenization_rag import RagTokenizer



@require_faiss
@require_torch
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: str         =tempfile.mkdtemp()
   lowerCamelCase__: Optional[int]         =8

   # DPR tok
   lowerCamelCase__: Dict         =[
       "[UNK]",
       "[CLS]",
       "[SEP]",
       "[PAD]",
       "[MASK]",
       "want",
       "##want",
       "##ed",
       "wa",
       "un",
       "runn",
       "##ing",
       ",",
       "low",
       "lowest",
   ]
   lowerCamelCase__: Optional[Any]         =os.path.join(self.tmpdirname			,					"dpr_tokenizer")
   os.makedirs(UpperCAmelCase_			,					exist_ok=UpperCAmelCase_)
   lowerCamelCase__: Any         =os.path.join(UpperCAmelCase_			,					DPR_VOCAB_FILES_NAMES["vocab_file"])
   with open(self.vocab_file			,					"w"			,					encoding="utf-8") as vocab_writer:
     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

   # BART tok
   lowerCamelCase__: Union[str, Any]         =[
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "\u0120",
       "\u0120l",
       "\u0120n",
       "\u0120lo",
       "\u0120low",
       "er",
       "\u0120lowest",
       "\u0120newer",
       "\u0120wider",
       "<unk>",
   ]
   lowerCamelCase__: Optional[int]         =dict(zip(UpperCAmelCase_			,					range(len(UpperCAmelCase_))))
   lowerCamelCase__: int         =["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
   lowerCamelCase__: List[str]         ={"unk_token": "<unk>"}

   lowerCamelCase__: Dict         =os.path.join(self.tmpdirname			,					"bart_tokenizer")
   os.makedirs(UpperCAmelCase_			,					exist_ok=UpperCAmelCase_)
   lowerCamelCase__: Any         =os.path.join(UpperCAmelCase_			,					BART_VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__: Tuple         =os.path.join(UpperCAmelCase_			,					BART_VOCAB_FILES_NAMES["merges_file"])
   with open(self.vocab_file			,					"w"			,					encoding="utf-8") as fp:
     fp.write(json.dumps(UpperCAmelCase_) + "\n")
   with open(self.merges_file			,					"w"			,					encoding="utf-8") as fp:
     fp.write("\n".join(UpperCAmelCase_))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->DPRQuestionEncoderTokenizer:



   '''simple docstring'''



   return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,					"dpr_tokenizer"))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->BartTokenizer:



   '''simple docstring'''



   return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,					"bart_tokenizer"))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Union[str, Any]:



   '''simple docstring'''



   shutil.rmtree(self.tmpdirname)

 @require_tokenizers
 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =os.path.join(self.tmpdirname			,					"rag_tokenizer")
   lowerCamelCase__: Optional[Any]         =RagConfig(question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict())
   lowerCamelCase__: Union[str, Any]         =RagTokenizer(question_encoder=self.get_dpr_tokenizer()			,					generator=self.get_bart_tokenizer())
   rag_config.save_pretrained(UpperCAmelCase_)
   rag_tokenizer.save_pretrained(UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         =RagTokenizer.from_pretrained(UpperCAmelCase_			,					config=UpperCAmelCase_)
   self.assertIsInstance(new_rag_tokenizer.question_encoder			,					UpperCAmelCase_)
   self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()			,					rag_tokenizer.question_encoder.get_vocab())
   self.assertIsInstance(new_rag_tokenizer.generator			,					UpperCAmelCase_)
   self.assertEqual(new_rag_tokenizer.generator.get_vocab()			,					rag_tokenizer.generator.get_vocab())

 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->List[str]:



   '''simple docstring'''



   lowerCamelCase__: int         =RagTokenizer.from_pretrained("facebook/rag-token-nq")
   lowerCamelCase__: str         =[
       "who got the first nobel prize in physics",
       "when is the next deadpool movie being released",
       "which mode is used for short wave broadcast service",
       "who is the owner of reading football club",
       "when is the next scandal episode coming out",
       "when is the last time the philadelphia won the superbowl",
       "what is the most current adobe flash player version",
       "how many episodes are there in dragon ball z",
       "what is the first step in the evolution of the eye",
       "where is gall bladder situated in human body",
       "what is the main mineral in lithium batteries",
       "who is the president of usa right now",
       "where do the greasers live in the outsiders",
       "panda is a national animal of which country",
       "what is the name of manchester united stadium",
   ]
   lowerCamelCase__: Dict         =tokenizer(UpperCAmelCase_)
   self.assertIsNotNone(UpperCAmelCase_)







 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
   lowerCamelCase__: Optional[int]         =[
       "who got the first nobel prize in physics",
       "when is the next deadpool movie being released",
       "which mode is used for short wave broadcast service",
       "who is the owner of reading football club",
       "when is the next scandal episode coming out",
       "when is the last time the philadelphia won the superbowl",
       "what is the most current adobe flash player version",
       "how many episodes are there in dragon ball z",
       "what is the first step in the evolution of the eye",
       "where is gall bladder situated in human body",
       "what is the main mineral in lithium batteries",
       "who is the president of usa right now",
       "where do the greasers live in the outsiders",
       "panda is a national animal of which country",
       "what is the name of manchester united stadium",
   ]
   lowerCamelCase__: List[Any]         =tokenizer(UpperCAmelCase_)
   self.assertIsNotNone(UpperCAmelCase_)



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






